Wydawnictwa Uczelniane / TUL Press
Stały URI zbioruhttp://hdl.handle.net/11652/17
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Pozycja Multi-task Learning for Classification, Segmentation, Reconstruction, and Detection on Chest CT Scans(Wydawnictwo Politechniki Łódzkiej, 2023) Hryniewska-Guzik, Weronika; Kędzierska, Maria; Biecek, PrzemysławLung cancer and COVID-19 have one of the highest morbidity and mortality rates in the world. For physicians, the identification of lesions is difficult in the early stages of the disease and time-consuming. Therefore, multi-task learning is an approach to extracting important features, such as lesions, from small amounts of medical data because it learns to generalize better. We propose a novel multi-task framework for classification, segmentation, reconstruction, and detection. To the best of our knowledge, we are the first ones who added detection to the multi-task solution. Additionally, we checked the possibility of using two different backbones and different loss functions in the segmentation task.Pozycja Transformers Neural Networks Applications in Different Computer Vision Tasks(Wydawnictwo Politechniki Łódzkiej, 2023) Brodzicki, Andrzej; Piekarski, Michał; Kostuch, Aleksander; Noworolnik, Filip; Aleksandrowicz, Maciej; Wójcicka, Anna; Jaworek-Korjakowska, JoannaTransformers architectures are one of the latest inventions in the field of deep learning. Originally dedicated to NLP, they begin to find use in computer vision too. In this paper, we briefly describe the idea behind vision transformers and present a few examples, where we utilised them in our research, focusing on the field of medical images and autonomous driving. We show, that vision transformers can be used in various tasks, such as detection or classification, as well as explain how some of their drawbacks can be mitigated with a transfer learning approach.